SampleStat - Articles

Hide your password in R

2017-12-20T10:51:12+01:00

There is several way to hide password in R. I recommend you to see this video if you want to have a more accurate picture on how to do it using fully encrypted password. There is specific libraries available in R which deal with encryption like sodium and also using XOR operator and others.

Mean of the n last or first

2017-06-01T02:00:00+02:00

A small post today that could be very useful. A colleague ask me how to compute the mean of the 5 oldest trees in a populations. The following code that I have found in stackoverflow.

parallelisation computing

2017-05-10T02:00:00+02:00

I already show you how to do for loop and nested for loop. When you want to do parallelisation computing, you simply replace the for command by the foreach command and %dopart%.

`.emacs` in org mode

2017-04-26T02:00:00+02:00

Usually, when you are using Emacs, all your favourite shortcut and others Emacs characteristic are in the .emacs file. However, to have a better .emacs organisation, I suggest you to use the org-mode with your .emacs. Like this, you can organise very easily your .emacs and put categories

Nested `for` loop

2017-03-24T01:00:00+01:00

I already talk about single for loop in a previous post. The same way we did for loop for a single object, we can do it multiple times.

data.table

2017-03-08T01:00:00+01:00

When you are doing a lot of R, you can find two different kind of people, the dplyr and the data.table people. Indeed, those to packages are used for table manipulation. They are like the ggplot2 function for graphic, when you know them they change your R experience and you cannot go back.

Workflow

2017-02-14T01:00:00+01:00

How you organise yourself when doing R will make it easier for you if you need to go back to your script weeks or years after, and to share with others people.

Creating a function

2017-01-22T01:00:00+01:00

In R, most of people used build-in functions which are very useful for all basics statistical analyses. When you need to do more complex statistics you can use packages. There is almost every current statistical functions and more in the CRAN repository. However, time to time you will need to build your own function. Usually, when I build a function is because for loop will not be efficient and that I need to put different parameters that I can change. I will take here an easy example that I have previously done for my work. I wanted to build a short function to draw maps and save them if I needed to.

For loop

2017-01-20T15:31:14+01:00

Using loop with R is not the proper behaviour to have. Indeed, using the apply functions family such as tapply, lapply, and so one are a better way to do it, but for me, this is in some case too complicated. The apply family functions improve the speed rate of T and the management of the memory. Thus, apply functions is relevant to use when you have big data-set to play with. Instead of using apply functions, I used for loop. This is simpler to understand it for me and with moderate data-set (about few thousand of rows and 100 columns) the gain of apply functions is limited. Like this, for loop can be read again quickly even when you comeback to your code years later. I will now provide a small example to understand when you can use for loop and how to used them. Also, note that understanding for loop will lead to use foreach function which allow parallelisation computing. It will be explain in another post.

Convert factor to character

2017-01-12T01:00:00+01:00

In R, you used sometimes functions to convert vectors to another format with as.character, as.factor or as.numeric.

Emacs + ESS = loving R

2016-12-30T01:00:00+01:00

Since my second year of university I have studied Statistic with R on Windows using the basic text editor from the R GUI console. I was happy with it for a couple of years. However, when I start coding more complex and longer function, it was a nightmare to handle human issues, specially parentheses tracking. A professor told me that they were some programs specific to code in R, such as Tinn-R or the well known R-Studio. I also found that Notepad++ as a plugin (nppToR) to link the text editor to R. However, after trying R-Studio and Notepad++, I was not very happy with them. I always did not like the multi-windows design from R-Studio, and Notepad++ was only a better way to use the script function from the R GUI. It was not fully integrated R. Finally, my professor told me about Emacs, and how it was working with R. I first try it with Windows, using the very good Emacs version from Vincent Goulet. It was exactly what I was looking for. A software that can interact with R without a lot of windows, and well integrated with R. Few years later, I jump to Linux using first Manjaro (an ArchLinux distribution), and after Xubuntu to keep the very good XFCE desktop environment.